Account sharing is a significant problem for online recommender systems to generate accurate personalized recommendations. To solve this problem, one not only has to identify whether an account is shared, but also needs to recognize the different users sharing that account. However, to generate relevant, personalized recommendations, the particular user under a shared account has to be correctly identified at the time of delivering the recommendations. In this paper, we address this problem by first identifying users behind each account using a projection based unsupervised method, and then learning a function which can predict a user's preference accurately based on their 'contextual' information. This approach allows us to generate personalized recommendations for each of the users sharing a single account. We empirically show that on real and synthetic data set our approach performs better than other state-of-the-art approaches.